A Level Computer Science Topic 9: Data Structures T eaching L ondon C omputing William Marsh School of Electronic Engineering and Computer Science Queen Mary University of London

Aims Where do lists and dictionaries come from? Understand the problem Introduce the following data structures Linked list Binary search tree Hash sets Graphs

Syllabus – OCR

Syllabus – AQA

Data Structures? “I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships.” Linus Torvalds, 2006

Problem: Arrays  Lists Abstractions

Abstraction v Implementation List abstraction Grows and shrinks – insert, remove Index into – lst[n] Stack abstraction (plate stack) Push, pop Simpler than list, as only access ‘top’ Many possible implementations of each abstraction Trade-offs

Lists in Arrays The array cannot grow To insert, we have to shuffle along Indexing is quick Because indexing is quick, there are implementations based on arrays in lists

Aside: Array and LMC Address of array – A – is address of entry 0 Address of A[n] is is A + n Real computers Have registers for addresses Do arithmetic on addresses... n Address of A

Set & Map Abstractions Set Membership Insert Remove Dictionary (Map) Keys are like a set – value attached to each key... Set operations Lookup value is similar to membership Key Value

Linked Lists An implementation of the list abstractions

Linked List Concept Each entry has A value A pointer to the next entry Keep a pointer to the front entry The pointer of the last entry is None bcdef frontback

Linked List Index Count along the list to entry i Return the value pointer = front count = 0 while count < i: pointer  next of current entry count = count + 1 return the value of current entry myList.index(i)

Linked List Update Count along the list to entry index Replace value with new value pointer = front count = 0 while count < idx: pointer  next of currentEntry count = count = 1 currentEntry.value = newValue myList.update(idx, newValue)

Linked List Insert Count along the list to entry idx-1 Insert a new entry Next pointer of current entry points to new entry Next pointer of new entry points to following entry myList.insert(idx, newValue)

Exercise Redraw list after: appending a new entry at the end inserting before entry zero inserting before entry 3 bcdef frontback

Linked List Code class Entry: def __init__(self, v): self.value = v self.next = None def setValue(self, v): self.value = v def getValue(self): return self.value def setNext(self, n): self.next = n def getNext(self): return self.next class List: def __init__(self): self.length = 0 self.first = None def append(self, value): entry = Entry(value) if self.first == None : self.first = entry return p = self.first q = p.getNext() while q != None: p = q q = p.getNext() p.setNext(entry) def index(self, i): count = 0 p = self.first while count < i: p = p.getNext() count = count + 1 return p.getValue()

Complexity of Linked List Indexing is linear: O(n) c.f. array index is O(1) need to do better! Often need to visit every entry in the list e.g. sum, search This is O(n 2 ) if we use indexing Easy to improve this by keeping track of place in list Search is O(n)

Binary Trees A more complex linked structure

Introduction Many uses of (binary) tree Key ideas Linked data structure with 2 (or more) links (c.f. linked list) Rules for organising tree Binary search tree Other uses Heaps Syntax trees

Binary Search Tree All elements to the left of any node are < than all elements to the right Root Right child Left child Leaf

Exercise: Put The Element In 17, 19, 28, 33, 42, 45, 48

Search Binary search E.g. if target is 28: 28 < 47 – go left 28 > 21 – go right What is the complexity of searching a binary tree?

Binary Tree Search Recursive algorithms Find-recursive(key, node): // call initially with node = root if node == None or node.key == key then return node else if key < node.key then return Find-recursive(key, node.left) else return Find-recursive(key, node.right)

Balance and Complexity Complexity depends on balance Left and right sub-trees (nearly) same size Tree no deeper than it need be Balanced Not balanced

Tree Traversal Order of visiting nodes in tree In-order Traverse the left subtree. Visit the root. Traverse the right subtree Pre-order Visit the root. Traverse the left subtree. Traverse the right subtree

Hash Sets

Insight Array are fast – indexing O(1) Map a string to an int, use as an array index hash() in Python Any hashable type can be a dictionary key Ideally, should spread strings out

Hash Table (Set) Look for string S in array at: hash(S) % size Collision Two items share a hash Linked list of items with same size Array length > number of items Array sized increased if table

Many Other Uses of Hashing Cryptography Checking downloads – checksum Checking for changes

Exercise Try Python hash function on strings and on other values Can you hash e.g. a person object?

Graphs

Graph Many problems can be represented by graphs Network connections Web links... Graphs have Nodes and edges Edges can be directed or undirected Edges can be labelled

Graph v Trees Only one path between two nodes in a tree Graph may have Many paths Cycles (loops)

Graph Traversal Depth first traversal Visit children,... then siblings Breadth first traversal Visit siblings before children Algorithms similar to trees traversal, but harder (because of cycles)

Graph Algorithms Shortest paths Final short path through graph with not-negative edge weight Routing in networks Polynomial Longest paths – travelling salesman Intractable

Graph Representations 2-D table of weights Adjacency list 1-D array of lists of neighbours

Exercise Show representation of in both formats

Exercise Show how maze can represented by a graph Maze solved by finding (shortest) path from start to finish

Summary Python has lists and dictionaries built in Data structures to implement these Linked data structures Hashing: magic